Textile‐Based Inductive Soft Strain Sensors for Fast Frequency Movement and Their Application in Wearable Devices Measuring Multiaxial Hip Joint Angles during Running

Advanced Intelligent Systems

Christopher Napier¹  JingYang Peng¹  Tyler J. Cuthbert¹  Mohammad Tavassolian¹  Carlo Menon¹ 

[1] Menrva Research Group Schools of Mechatronic Systems & Engineering Science Simon Fraser University Metro Vancouver BC V5A1S6 Canada;

关键词: inductive sensors;    kinematic tracking;    smart sensors;    soft sensors;    wearable devices;   

DOI  :  10.1002/aisy.201900165

来源: DOAJ

【 摘 要 】

Wearable multiaxes motion tracking with inductive sensors and machine learning is presented. The production, characterization, and use of a modular and size‐adjustable inductive sensor for kinematic motion tracking are introduced. The sensor is highly stable and able to track high‐frequency (>15 Hz) and high strain rates (>450% s−1). Four sensors are used to fabricate a pair of motion capture shorts. A random forest machine learning algorithm is used to predict the sagittal, transverse, and frontal hip joint angle, using the raw signals from sport shorts during running with a cohort of 12 participants against a gold standard optical motion capture system to an accuracy as high as R2 = 0.98 and root mean squared error of 2° in all three planes. Herein, an alternative strain sensor is provided to those typically used (piezoresistive/capacitive) for soft wearable motion capture devices with distinct advantages that can find applications in smart wearable devices, robotics, or direct integration into textiles.

【 授权许可】

Unknown